The present invention relates to a detector and classifier for signals that are the additive combination of a few constant-amplitude sinusoidal components, hereafter called N-tones. The invention herein has particular application to the sub-class of N-tones called dual-tone multi-frequency (DTMF) telephone signals.
DTMF signals are N-tones used for representing telephone numbers and other signalling functions within the telephone system. Detailed specification of DTMF signal properties have been standardized by international agreement. Sixteen unique DTMF signals are defined; one for each of the numbers on a telephone keypad plus six for additional keys. Ignoring noise, distortion and allowable equipment variability, each DTMF signal is an additive combination of two equal-amplitude tones. The frequencies of the component tones serve to distinguish one DTMF signal from another. Specifically, each DTMF signal is comprised of two tones with frequencies taken from two mutually-exclusive frequency bands. For example, the signal generated by depressing "1" on the telephone keypad is the sum of a 697 Hz tone and a 1209 Hz tone, and the signal generated by depressing "5" is the sum of a 770 Hz tone and a 1336 Hz tone. The low frequency band, or low-band, is comprised of tones with frequencies of (nominally) 697 Hz, 770 Hz, 852 Hz and 941 Hz. The high frequency band, or high-band, is comprised of tones with frequencies of (nominally) 1209 Hz, 1336 Hz, 1477 Hz and 1633 Hz.
In telephony applications, one must be able to quickly detect and accurately classify DTMF signals that are embedded in noise, and one must not falsely indicate DTMF presence within other valid signals. The second issue generally presents the largest challenge because short segments of speech occasionally appear very DTMF-like.
The common approaches to DTMF detection are band-pass filtering, parametric modelling and zero-crossing analysis. The filter-based detectors use band-pass filters to isolate the strongest spectral components in the signal, and then either directly or indirectly test the frequency, purity and relative magnitude of the components. Detectors that use the discrete Fourier transform (DFT) fall under this category because the DFT can be viewed as a bank of narrow band-pass filters each followed by a magnitude estimator. The most common modelling method is linear prediction, an advantage of which is computational efficiency. Splitting of the input signal into two monotonal streams reduces the complexity and allows for the use of lower-order predictors. The principle advantage of the zero-crossing approach is its ease of implementation using simple integrated hardware.